---
title: "Coronavirus"
author: "Rubén F. Bustillo"
date: '2020-03-04'
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: fill
theme: cerulean
---
```{r setup, include=FALSE}
#------------------ paquetes ------------------
devtools::install_github("RamiKrispin/coronavirus")
library(flexdashboard)
library(coronavirus)
library(tidyverse)
library(echarts4r)
library(DT)
data(coronavirus)
# COLORES:
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "lightsteelblue"
active_color <- "orange"
recovered_color <- "limegreen"
death_color <- "red"
# DATASETS:
# confirmed/active/recovered/death cases by country
df <- coronavirus %>%
group_by(Country.Region, type) %>%
summarise(total = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
arrange(-confirmed) %>%
ungroup() %>%
mutate(country = if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
mutate(country = if_else(country == "Mainland China", "China", country)) %>%
mutate(country = if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
mutate(country = if_else(country == "Dominican Republic", "Dominican Rep.", country)) %>%
mutate(country = trimws(country)) %>%
mutate(country = factor(country, levels = country))
df_spain <- df %>%
filter(country == "Spain")
# CUMULATIVE CASES:
df_daily <- coronavirus %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
df1 <- coronavirus %>%
filter(date == max(date))
df_map <- df %>%
mutate(country = recode_factor(country,
`US` = "United States",
`UK` = "United Kingdom",
`UAE` = "United Arab Emirates",
`South Korea`= "Korea"))
```
Sidebar {.sidebar}
=======================================================================
### Last Update {.value-box}
```{r}
valueBox(value = head(df1$date, n=1),
caption = "Last update")
```
\
\
\
This dashboard presents information on COVID-19. The {coronavirus} dataset package is available on CRAN and comes from the [John Hopkins University Center for Systems Science and Engineering](https://hub.jhu.edu/2020/01/23/coronavirus-outbreak-mapping-tool-649-em1-art1-dtd-health/). The {coronavirus} dataset is updated daily by @Rami_Krispin (https://ramikrispin.github.io/coronavirus/)).
I take this opportunity to thank Rami Krispin for sharing his work and code with the #rstat community. His dashboard on coronavirus has helped me tremendously to improve my knowledge of the {flexdashboard} package.
[www.rquer.netlify.com](https://rquer.netlify.com/)
Dashboard
=======================================================================
Row
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","),
" (", round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","),
" (", round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","),
" (", round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Death Cases",
color = death_color)
```
Row {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Confirmed Cases
```{r}
df_map %>%
e_charts(country) %>%
e_map(confirmed) %>%
e_title("Confirmed Cases by Country", left= "center") %>%
e_visual_map() %>%
e_theme("infographic") %>%
e_visual_map_range(selected= list(0,200))
```
### Active Cases
```{r}
df_map %>%
e_charts(country) %>%
e_map(unrecovered) %>%
e_title("Active Cases by Country", left= "center") %>%
e_visual_map() %>%
e_theme("infographic")%>%
e_visual_map_range(selected= list(0,200))
```
### Recovered Cases
```{r}
df_map %>%
e_charts(country) %>%
e_map(recovered) %>%
e_title("Recovered Cases by Country", left= "center") %>%
e_visual_map() %>%
e_theme("infographic") %>%
e_visual_map_range(selected= list(0,200))
```
### Death Cases
```{r}
df_map %>%
e_charts(country) %>%
e_map(death) %>%
e_title("Death Cases by Country", left= "center") %>%
e_visual_map() %>%
e_theme("infographic") %>%
e_visual_map_range(selected= list(0,200))
```
Data
=======================================================================
Column
-------------------------------------
###
```{r}
df_table <- df %>%
select(country, confirmed, recovered, death)
df_table %>%
datatable(rownames = FALSE)
```
### Locations of people killed by COVID-19
```{r, fig.width=10, fig.height=7}
mapa_mundo <- map_data("world")
coronavirus_mapa <- coronavirus %>%
filter(type == "death")
mapa_mundo %>%
ggplot() +
geom_polygon(aes(x=long, y=lat, group=group),
fill= "grey30",
color= "white") +
geom_point(data = coronavirus_mapa,
aes(x = coronavirus_mapa$Long,
y = coronavirus_mapa$Lat,
color = "orange",
alpha = 0.7,
size = 1.2)) +
theme_classic() +
theme(
axis.line = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
legend.position = "none"
)
```
Column
-------------------------------------
###
```{r}
plotly::plot_ly(data = df,
x = ~ country,
y = ~ unrecovered,
type = "bar",
name = "Unrecovered",
marker = list(color = "darkorange")) %>%
plotly::add_trace(y = ~ recovered,
name = "Recovered",
marker = list(color = recovered_color)) %>%
plotly::add_trace(y = ~ death,
name = "Death",
marker = list(color = death_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases (log)",
type = "log"),
xaxis = list(title = ""),
hovermode = "compare")
```
Cumulative Data
=======================================================================
Row
-------------------------------------------------------------------------
### Cumulative Confirmed Cases
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed_cumulative,
type = "bar",
name = "Active",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases (cumulative)"),
xaxis = list(title = ""))
```
### Cumulative Active Cases
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(yaxis = list(title = "Active Cases (cumulative)"),
xaxis = list(title = ""))
```
Row
-------------------------------------------------------------------------
### Cumulative Recovered Cases
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Cumulative Death Cases
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
Spain
=======================================================================
Column
-------------------------------------
###
```{r, figh.height = 10}
df_Spain <- coronavirus %>%
filter(Country.Region == "Spain") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
df_Spain %>%
datatable(rownames = FALSE)
```
### Confirmed Cases in Spain
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_Spain) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = df_spain$confirmed,
caption = "Confirmed Cases in Spain",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = df_spain$unrecovered,
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = df_spain$recovered,
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = df_spain$death,
caption = "Death Cases",
color = death_color)
```
